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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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At least 199 records · Page 11

An Implanted, Stimulated Muscle Powered Piezoelectric Generator

A totally implantable piezoelectric generator system able to harness power from electrically activated muscle could be used to augment the power systems of implanted medical devices, such as neural prostheses, by reducing the number of battery replacement surgeries or by allowing periods of untethered functionality. The features of our generator design are no moving parts and the use of a portion of the generated power for system operation and regulation. A software model of the system has been developed and simulations have been performed to predict the output power as the system parameters were varied within their constraints. Mechanical forces that mimic muscle forces have been experimentally applied to a piezoelectric generator to verify the accuracy of the simulations and to explore losses due to mechanical coupling. Depending on the selection of system parameters, software simulations predict that this generator concept can generate up to approximately 700 W of power, which is greater than the power necessary to drive the generator, conservatively estimated to be 50 W. These results suggest that this concept has the potential to be an implantable, self-replenishing power source and further investigation is underway.

Lewandowski, Beth↗

Electrochemical Hydrogen Peroxide Generator

Two-electron reduction of oxygen to produce hydrogen peroxide is a much researched topic. Most of the work has been done in the production of hydrogen peroxide in basic media, in order to address the needs of the pulp and paper industry. However, peroxides under alkaline conditions show poor stabilities and are not useful in disinfection applications. There is a need to design electrocatalysts that are stable and provide good current and energy efficiencies to produce hydrogen peroxide under acidic conditions. The innovation focuses on the in situ generation of hydrogen peroxide using an electrochemical cell having a gas diffusion electrode as the cathode (electrode connected to the negative pole of the power supply) and a platinized titanium anode. The cathode and anode compartments are separated by a readily available cation-exchange membrane (Nafion 117). The anode compartment is fed with deionized water. Generation of oxygen is the anode reaction. Protons from the anode compartment are transferred across the cation-exchange membrane to the cathode compartment by electrostatic attraction towards the negatively charged electrode. The cathode compartment is fed with oxygen. Here, hydrogen peroxide is generated by the reduction of oxygen. Water may also be generated in the cathode. A small amount of water is also transported across the membrane along with hydrated protons transported across the membrane. Generally, each proton is hydrated with 3-5 molecules. The process is unique because hydrogen peroxide is formed as a high-purity aqueous solution. Since there are no hazardous chemicals or liquids used in the process, the disinfection product can be applied directly to water, before entering a water filtration unit to disinfect the incoming water and to prevent the build up of heterotrophic bacteria, for example, in carbon based filters. The competitive advantages of this process are: 1. No consumable chemicals are needed in the process. The only raw materials needed are water and oxygen or air. 2. The product is pure and can therefore be used in disinfection applications directly or after proper dilution with water. 3. Oxygen generated in the anode compartment is used in the electrochemical reduction process; in addition, external oxygen is used to establish a high flow rate in the cathode compartment to remove the desired product efficiently. Exiting oxygen can be recycled after separation of liquid hydrogen peroxide product, if so desired. 4. The process can be designed for peroxide generation under microgravity conditions. 5. High concentrations of the order of 6-7 wt% can be generated by this method. This method at the time of this reporting is superior to what other researchers have reported. 6. The cell design allows for stacking of cells to increase the hydrogen peroxide production. 7. The catalyst mix containing a diquaternary ammonium compound enabled not only higher concentration of hydrogen peroxide but also higher current efficiency, improved energy efficiency, and catalyst stability. 8. The activity of the catalyst is maintained even after repeated periods of system shutdown. 9. The catalyst system can be extended for fuel-cell cathodes with suitable modifications.

Tennakoon, Charles L. K.↗

Testing to Characterize the Advanced Stirling Radioisotope Generator Engineering Unit

The Advanced Stirling Radioisotope Generator (ASRG), a high efficiency generator, is being considered for space missions. Lockheed Martin designed and fabricated an engineering unit (EU), the ASRG EU, under contract to the Department of Energy. This unit is currently undergoing extended operation testing at the NASA Glenn Research Center to generate performance data and validate life and reliability predictions for the generator and the Stirling convertors. It has also undergone performance tests to characterize generator operation while varying control parameters and system inputs. This paper summarizes and explains test results in the context of designing operating strategies for the generator during a space mission and notes expected differences between the EU performance and future generators.

Lewandowski, Edward↗

SSL Freeform Generator v1.00

The Orion spacecraft utilizes a system of Compact Unique Identifiers (CUIs) to identify telemetry measurements in the process of communicating with the ground. While avionic systems are still in development, simulation of telemetric data must be used for the development of dependent systems. The SuperScript Language (SSL) developed by Honeywell Aerospace provides users a programming language for simulation of this data. The use of tools that provide SSL generation capabilities at a higher level allows for faster and more efficient generation of simulation waveforms. Previous SSL Generator implementations such as Orion Scripted Interface Generator (OrionSIG) relied on software supported routines to generate simulation points for the SSL scripts. This provided relevant simulation capabilities, but was reduced in flexibility by the available software routines available. For this approach, software supported routines have been entirely dropped. Rather, the user is required to enter their own simulation waveform input. This allows arbitrarily complex functions to be utilized simulated with maximum degree of freedom for the user.: The idea for this version of the SSL Generator was a mapping of arbitrary waveforms to Orion CUI telemetry data. The SSL Generator implemented offers a higher degree of freedom to the user than previous versions while attempting to maintain abstraction from the underlying SSL script and the Orion CUI data types. As such, many mappings of waveforms to CUIs will simply not work. Certain implementation decisions were made to minimize the damage caused by these unintended side-effects, such as saturation of values exceeding the maximum or minimum bounds of the data type. Other details, such as signed values being represented in unsigned data types, present problems which cant be fixed to an acceptable degree of intended behavior without user input. Awareness of the limitations of all layers of the software stack will increase the productivity of the end-user.

telemetry↗

Three Generations of Tracking and Data Relay Satellite (TDRS) Spacecraft

The current Tracking and Data Relay Satellite configuration consists of nine in-orbit satellites (four first generation, three second generation and two third generation satellites) globally distributed in geosynchronous orbit to provide near continuous data relay service to missions like Hubble Space Telescope and the International Space Station. The 1st generation spacecraft were designed by TRW/Northrop Grumman with their launches of the five spacecraft ranging from 1983 through 1995. The 2nd and 3rd generation spacecraft were designed by Boeing with their launches ranging 2000 - 2002 and 2013 - 2017 respectively. TDRS-3 is now 27 years on orbit, continues to be a capable asset for the TDRS constellation. Lack of need for inclination control combined with large fuel reserves and redundancy on critical elements provides spacecraft that operate well past design life, all of which contributes to expanded TDRS constellation support capabilities. All spacecraft generations have issues. Significant issues will be summarized with the focus on the Boeing related problems. Degradations and failures are continually assessed and provide the foundation for yearly updates to spacecraft reliability models, constellation service projections and deorbit plans (in order to meet NASAs mandate of limiting orbital debris). Even when accounting for degradations and failures, the life expectancy for the Boeing delivered 2nd generation TDRS-8, 9 10 TDRS are anticipated to be 25+ years.

TDRS↗

A Comparison of Three Random Number Generators for Aircraft Dynamic Modeling Applications

Three random number generators, which produce Gaussian white noise sequences, were compared to assess their suitability in aircraft dynamic modeling applications. The first generator considered was the MATLAB (registered) implementation of the Mersenne-Twister algorithm. The second generator was a website called Random.org, which processes atmospheric noise measured using radios to create the random numbers. The third generator was based on synthesis of the Fourier series, where the random number sequences are constructed from prescribed amplitude and phase spectra. A total of 200 sequences, each having 601 random numbers, for each generator were collected and analyzed in terms of the mean, variance, normality, autocorrelation, and power spectral density. These sequences were then applied to two problems in aircraft dynamic modeling, namely estimating stability and control derivatives from simulated onboard sensor data, and simulating flight in atmospheric turbulence. In general, each random number generator had good performance and is well-suited for aircraft dynamic modeling applications. Specific strengths and weaknesses of each generator are discussed. For Monte Carlo simulation, the Fourier synthesis method is recommended because it most accurately and consistently approximated Gaussian white noise and can be implemented with reasonable computational effort.

Grauer, Jared A.↗

Plans and Concepts for a New Generation of RTGs for Planetary Science Missions

Of the six types of radioisotope thermoelectric generators NASA has flown in space, only the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) is currently available for spaceflight, and it relies on technology first used for RTGs in the 1970s. The MMRTG is a rugged power system capable of delivering 110W at launch. NASA is considering future missions with higher power demands however, and sponsored a study to identify concepts and plans to address those needs. The Director of NASA’s Planetary Sciences Division was briefed on a potential enhancement to the MMRTG in late-2012, just a couple of days after the landing of the Mars Science Laboratory rover, Curiosity, on Mars. NASA subsequently funded system-level engineering and technology maturation tasks for a proposed enhanced MMRTG, or eMMRTG, in fiscal year 2013. There was no plan to build a complete generator, rather the engineering of one, and the transfer of technology from laboratories at the Jet Propulsion Laboratory to industry were begun. NASA has now formed a Project to take the eMMRTG from technology to a qualification unit. This would form the first of a new generation of RTGs in 50 years. NASA has also formed a Project for a Next-Generation RTG concept based upon a study led by this author and a large team. NASA’s Radioisotope Power Systems (RPS) Program set the objective for the study to explore what possible options NASA has for Next-Generation-RTGs. The scope and breadth of the study included many possible destinations within the solar system, and traded a variety of RTG conceptual designs, and risk rated a variety of thermoelectric materials and couple configurations. Requirements were defined for the RTG concepts, a variety of thermoelectric materials were evaluated to find the most mature candidates, and performance was estimated for each RTG concept that could use the most mature of these new thermoelectric materials. The study relied upon mission concepts outlined in the latest Planetary Science Decadal Survey (2011), other more recent mission studies completed throughout the agency, and recent analyses of potential missions to ocean worlds to identify requirements that were not applied to previous RTGs but might prove valuable to these NG-RTGs. RTG concepts with maximal potential utility were identified as being modular and ranging in power output from 50 to 500W. A variety of RTG design concepts with several distinguishing characteristics were formulated. The plans and concepts for a new generation of RTGs (the eMMRTG and NG-RTG) will be discussed.

Woerner, David F.↗

In-Situ Electrochemical Generation and Utilization of Hydrogen Peroxide for Disinfection

Disinfection needs to meet personal hygiene requirements at the International Space Station (ISS) is currently accomplished through the use of pre-packaged, disposable, wetted wipes, which represent an appreciable carry-along mass and disposal burden. However, as human missions travel further into the solar system the availability of resources to resupply will be diminished. Therefore, next-generation system should use onboard utilities to create on demand disinfectants, thereby reducing the dependence on earth-based supplies and further eliminating storage and disposal problems. Accordingly, we are developing an in-situ approach to electrochemically generate hydrogen peroxide disinfecting solution utilizing onboard life support supplies (Air/Water) to neutralize surface microorganisms present in closed living systems. As discussed within our 2019 and 2021 ICES papers (ICES-2019-38 and ICES-2021-273), we have continued to improve our TRL by scaling the electrochemical generation production process and validating the system in a zero-gravity parabolic loop flight test. In this paper we will demonstrate a system that can achieve over 1 L of >2 w/w% peroxide per day with DI water and air feeds. The hydrogen peroxide solutions generated were sent to NASA for microbial control property characterization. Overall, the electrochemical peroxide generation system offers a more economical and practical alternative, with the disinfectant being generated on demand and in-situ (using available life support materials of Air/Water); and applied to reusable cloths. The specific application of interest to this program is crew contact surfaces in space vehicles, but this technology could be utilized for waste water disinfection, heat exchanger biofouling remediation, and laundry applications. The peroxide generation system will also be able to address Earth-based needs in various settings such as field hospitals, restaurants, military, warehouses, movie theatres, among many others.

hydrogen peroxide↗

Assessing Climate Change-Induced Variability in Generation Potential and Droughts of Renewable Energy Systems in India

Solar photovoltaic (PV) and wind energy systems are crucial for decarbonizing the electricity sector and achieving climate goals. However, these systems are weather-dependent, and ignoring the potential changes in their generation levels due to climate change could compromise achieving climate targets and meeting future electricity demand. This study evaluates the impact of climate change on the generation potential of wind and solar PV systems in India for three future periods, 2030 (2021-2040), 2050 (2041-2060), and 2070 (2061-2080) compared to the baseline year 2000 (1991-2010), under three emission scenarios: SSP245, SSP370, and SSP585. Solar PV generation levels consistently decline (up to 10 %) across all regions and scenarios. Wind energy shows more pronounced variability (-20 % to 30 %). The South and Southeastern regions of India show improvements in wind potential across all scenarios and time periods. This study also investigated the projected changes in the generation droughts of both energy systems. For solar PV, drought days increase across most regions (exceeding 500 days under SSP370 across the 20-year period). In contrast, wind energy sees a reduction in drought days, especially in parts of South and Southeast India (declines exceeding 50 days across different scenarios). For both energy systems, the patterns of generation drought and generation potential are similar, and indicate that Western and Northern India may be less favorable for the future expansion of solar PV and wind energy, respectively. These results highlight the need to account for the potential impacts in future capacity planning.

14 SOLAR ENERGY↗

Understanding the heat generation mechanisms and the interplay between joule heat and entropy effects as a function of state of charge in lithium-ion batteries

The thermal performance of lithium-ion battery cells is critical for ensuring their safe and reliable operation across various applications. In this study, we employed an isothermal calorimetry method to investigate the heat generation of commercial 18650 lithium-ion battery fresh cells during charge and discharge at different current rates, ranging from 0.05C to 0.5C, and across various temperatures: 20 °C, 30 °C, 40 °C, and 50 °C. Our findings revealed a direct correlation between heat generation and current rates, indicating that higher current rates lead to increased heat generation within the cells. Conversely, we observed that heat generation remained relatively stable as the temperature rose, suggesting that temperature changes within this range may not significantly impact the heat generation of fresh cells during typical operations. Furthermore, our study explored irreversible heat generation, which depends on the applied current and overpotential, using the galvanostatic intermittent titration technique at 0.05C–0.5C and 30 °C. Additionally, electrochemical impedance spectroscopy was performed on the same cells during charge and discharge at 20 °C, 30 °C, and 40 °C to analyze cell impedance. Finally, our results indicated a consistent dependence of impedance on the state of charge and depth of discharge, with a significant increase in impedance observed at the end of the discharge process.

25 ENERGY STORAGE↗

TRIM: AI Guided Random Number Generation for Resource-Constrained IoT Systems

Random numbers often serve as the backbone for many security solutions in diverse domains such as cryptography, side channel leakage prevention, and moving target defense. However, generating true random numbers requires a physical source of entropy (e.g. hardware, quantum, environmental phenomenon) making it difficult to realize at a large scale and at a low cost. On the flip side, pseudorandom number generators (easy to implement) following a specific distribution (e.g. Gaussian) can be easily compromised given a sufficient amount of traces. In this work, we have developed a machine learning-guided generative approach that can be used to create portable, resource-efficient, and cost-effective random number generators with high throughput and true randomness characteristics. We implement the proposed approach as a highly parameterized framework and perform extensive evaluation for different settings. The framework was able to learn from true random sources such as irrational numbers and environmental audio noise and imitate those sources towards generating new good quality random numbers on demand. We have generated more than 1 billion bits and observed robust performance in terms of true randomness metrics obtained from NIST SP 800-22 and FIPS 140-1 randomness test suites achieving a throughput of up to 142.85 Mbps. Compared to the state-of-the-art (SOTA) technique, the iso-cost setup of our framework can achieve more than 500 Mbps in a distributed setting. We have evaluated the efficacy of running the true randomness imitation AI models on target edge devices such as Raspberry Pi 4 (Model B), Nvidia Jetson Nano, Nvidia Jetson Orin Nano and Nvidia Jetson Xavier. We have also looked at the security of the TRIM framework itself against different adversarial threat models.

Cybersecurity↗

Stochastic Adaptive Droop Control in Frequency Regulation of Power Systems With Intermittent Generators

Modern power systems (MPSs), including microgrids (MGs), are increasingly incorporating multiple renewable energy sources (RESs) such as wind and solar power, as well as battery storage and controllable loads. While environmentally beneficial, these sources pose challenges for control and management due to their intermittent and stochastic nature, especially in maintaining frequency stability with multiple interconnected generators of varying capacities. Traditional droop control methods are effective in systems with generators that are dispatchable and have fixed generation capacities, but they fall short when applied to systems with RESs, where generation capacities are dynamic and affected by unpredictable environmental conditions. To address these challenges, this paper introduces a novel stochastic adaptive droop control (SADC) method for load frequency control (LFC). The proposed method adapts droop coefficients in real time, based on the measured stochastic data of power generation capacities, enabling more effective frequency regulation in systems with variable and intermittent power generation. Unlike traditional adaptive control methods, which assume constant or slowly-varying system parameters, this approach accounts for stochastic processes by modeling them as Markov chains, enabling robust performance under highly dynamic and unpredictable conditions. The key contributions of this work include the development of real-time droop coefficient adaptation algorithms, derivation of their stability and convergence properties, and the demonstration of the advantages of the method through simulations. Case studies highlight the improved performance of frequency regulation, particularly in addressing the impact of stochastic weather conditions and the benefits of reducing dependence on battery reserves in dealing with intermittency of RESs. Finally, this paper provides a comprehensive analysis of the theoretical foundations of the method, as well as practical implementation insights for future power systems with high penetration of RESs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

autonomous vehicle↗

Generating Electricity with Hydraulically Amplified Self-Healing Electrostatic (HASEL) Transducers

This study identifies hydraulically amplified self-healing electrostatic (HASEL) transducers as electricity generators, contrary to their conventional role as actuators. HASELs are soft, variable-capacitance transducers inspired by biological muscles which were developed to mimic the flexibility and functionality of natural muscle tissues. This research characterizes HASELs as generators by reversing their energy conversion mechanism—generating electricity through mechanical deformation. The study assesses the practical laboratory performance of HASELs by analytic modeling and experimental evaluation. Outcomes of the study include the following: (i) up to 2.5 mJ per cycle per 50 mm wide HASEL pouch of positive net energy generation in experimental testing—corresponding to an energy density of 2.0 mJ cm−3; (ii) a maximum theoretical energy density of 4.2 mJ cm−3; (iii) the electromechanical characteristics governing efficient conversion; and (iv) design considerations to enhance HASEL generator performance in future applications. This study broadens HASEL’s applicability and utility as a multi-functional transducer for renewable energy and general adaptive electricity generation.

13 HYDRO ENERGY↗

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗

Intergenerational Inequities in Exposure to Climate Extremes: Young Generations Are Severely Threatened By Climate Change

Under continued global warming, extreme events such as heat waves will continue to rise in frequency, intensity, duration, and spatial extent over the next decades. Younger generations are therefore expected to face more such events across their lifetimes compared with older generations. This raises important issues of solidarity and fairness across generations that have fueled a surge of climate protests led by young people in recent years and that underpin issues of intergenerational equity raised in recent climate litigation. However, the standard scientific paradigm is to assess climate change in discrete time windows or at discrete levels of warming, a “period” approach that inhibits quantification of how much more extreme events a particular generation will experience over its lifetime compared with another. By developing a “cohort” perspective to quantify changes in lifetime exposure to climate extremes and compare across generations (see the first figure), we estimate that children born in 2020 will experience a two- to sevenfold increase in extreme events, particularly heat waves, compared with people born in 1960, under current climate policy pledges. Our results highlight a severe threat to the safety of young generations and call for drastic emission reductions to safeguard their future. Meteorological extremes, hazards, or climate change impacts are mostly studied as they evolve over time under varying emission scenarios and socioeconomic pathways. For example, applying a heat wave indicator (see table S1) to four bias-adjusted global climate models indicates that the land area annually affected by such heat waves will increase from ~15% around 2020 to ~22% by 2100 under a scenario compatible with limiting global warming to 1.5°C, and to ~46% under a scenario in line with current emission reduction pledges (see the first figure). Recent studies extended this approach, studying aspects of climate change as a function of global mean temperature (GMT) increments, highlighting the scenario-independence of several extreme event indicators but remaining, in essence, a comparison of time windows. By contrast, we performed a birth cohort analysis by combining a collection of multimodel extreme event projections with country-scale life expectancy information, gridded population data, and future global temperature trajectories from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5°C (see supplementary materials). By integrating the exposure of an average person in a country or region to extreme events across their lifetime, we encapsulate spatiotemporal changes in climate hazards, population density, cohort size, and life expectancy (see the first figure).

global warming↗

Distributed Automatic Generation Control Considering DPV Using T&D Dynamic Co-Simulation

The increasing adoption of distributed energy resources (DERs) over the last decade warrants a reconsideration of control of generation resources. This paper proposes a distributed Automatic Generation Control (AGC) using transmission-and-distribution (T&D) dynamic co-simulation framework for the efficient DPV frequency regulation services. The co-simulation framework allows AGC units to exchange the information for distributed AGC, based on their adopted communication network topology. As a result, a cost-effective automatic generation control is achieved with DPV and conventional generators. The proposed distributed AGC is based on the gossip algorithm in which the neighboring AGC units share the relevant local information with each other and updates their share of AGC regulation signal. Distributed photovoltaics (DPV) unit contribute to AGC response based on their headroom capacity via DER aggregators. The algorithm is tested on IEEE-14 bus transmission system under conditions of generation failure and random load variation to observe effective frequency regulations service offered by DPVs and other AGC units. The study shows that DPV can effectively participate in AGC with the proposed distributed control framework.

automatic generation control↗

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗