About

Bogdan Popa is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan. His research focuses on the design, optimization, and dynamics of new generations of engineered materials, specifically metamaterials that enable improved control over the propagation of acoustic, elastic, and electromagnetic waves. His work has applications in biomedical engineering, telecommunications, aero- and underwater acoustics, noise control, smart materials, and mechatronics. He has been recognized for his contributions to active acoustic metamaterials, including being elected as a Fellow of the Acoustical Society of America. His research also involves understanding biosonar in nature, working with dolphins to better understand their biosonar, which has benefits for healthcare, the environment, and automotive industries. Bogdan Popa has received the NSF CAREER Award, a prestigious recognition supporting early-career faculty with potential for significant research and educational impact. His research group continues to explore innovative solutions in acoustics and material science, contributing to advancements in noise control and wave propagation technologies.

Research topics

• Environmental science
• Computer science
• Engineering
• Materials science
• Environmental economics

Selected publications

• Complete characterization of Willis materials from measured acoustic scattered fields with iterative convolutional neural networks

  2026-04-16

  articleSenior author

  Characterizing acoustic Willis materials offer unique opportunities in medical diagnostics, structural health monitoring, and nondestructive evaluation as they better model the complex materials with various levels of asymmetry involved in these applications. However, current estimation methods are insufficient for fully characterizing these materials due to the limited knowledge of the complex wave-matter interactions in Willis media. We introduce an iterative convolutional neural network (CNN) training scheme that robustly maps the complete set of material and geometric parameters of Willis samples to their scattered sound fields. The synthetically-trained CNN estimates all unknown parameters from measured acoustic scattering data from which statistical confidence bounds are derived. The process is repeated iteratively by searching in the newly derived and progressively smaller parameter space until the desired precision is achieved. This non-uniform and adaptive sampling strategy results in more accurate and efficient characterization for advanced health monitoring and related applications.

  PublisherDOI

• A Novel Approach to Determining the Turbine Discharge at Hydropower Plants with Adduction Channel

  2025-02-18

  book-chapterSenior author

  Discharge is one of the most important parameters that characterize the operation of a hydroelectric power plant. Traditional methodologies determine the discharge of a high-head hydropower plant by the Gibson method, by exploring the velocity field through a penstock section (current meter method). Nevertheless, it is much more comfortable and advantageous to measure the discharge in a downstream section, at the immediately following hydropower plant, but in this case, it is necessary to know all the phenomena that occur in the tailrace channel between the two hydropower plants and to establish the measurement conditions so that the discharge estimation be within the limits imposed by the standards in force. This paper aims to determine the turbine discharge at Retezat HPP by measuring the discharge to the turbine from the HA1 in Clopotiva HPP.

  PublisherDOI

• MULTICRITERIA EVALUATION AND DECISION SCENARIOS FOR THE INTEGRATION OF THE LIMBERG III HPP INTO THE ENERGY SYSTEM

  International Multidisciplinary Scientific GeoConference SGEM ... · 2025-12-27

  articleSenior author

  This study presents a multicriteria evaluation for the integration and development of the Limberg Pumped Storage Hydropower Plant (PSHP) in Austria. The research aims to identify the most sustainable and efficient solution to enhance energy storage capacity and operational flexibility within the national energy system. Three development scenarios were analyzed using a Multicriteria Decision Analysis (MCDA) based on the Analytic Network Process (ANP) implemented in Super Decisions (SD) software. Scenario 1 involves the rehabilitation of Limberg II and the enlargement of the lower reservoir by raising the dam crest. Scenario 2 proposes the construction of a new hydropower unit, Limberg III. Scenario 3 combines both strategies, involving the construction of Limberg III together with the dam crest elevation to increase storage capacity and system efficiency. Scenario 3 stands out as the most viable and resilient option from both technical and strategic perspectives. Its mixed development nature, integrating new infrastructure with modernization of existing facilities, ensures not only the highest analytical score obtained through the ANP-based evaluation but also the stability of this position under varying decision parameters.

  PublisherDOI

• Hydropower–FPV Hybridization for Sustainable Energy Generation in Romania

  Water · 2025-11-01 · 1 citations

  articleOpen accessSenior author

  This paper investigates the integration of hydropower and solar energy within the Lower Olt River cascade as a pathway toward sustainable energy generation in Romania. The study focuses on the conceptual design of future hybrid power plants consisting of existing hydropower facilities where floating photovoltaic panels are proposed to be installed on the reservoir’s surfaces. An estimation of electricity production from both sources was performed, followed by the formulation of a trading strategy for the July–September 2025 period. The paper also explores the interaction between tactical and strategic management in hydropower operation and planning, describing how forecasting and decision-making processes are structured within the institutional framework. Finally, results for the selected hydropower plants demonstrate the positive influence of floating photovoltaic deployment on company performance, the national energy mix, and the overall sustainability of energy generation in Romania.

  PublisherOA PDFDOI

• PROGRAMMING TOOLS FOR INFLOWS FORECASTING IN HYDROPOWER RESERVOIRS

  International Multidisciplinary Scientific GeoConference SGEM ... · 2025-12-27

  article1st authorCorresponding

  Within the framework of the iAMP-Hydro EU project, two dedicated work packages address the challenges of inflow and power forecasting for both run-of-river hydropower plants and large hydropower reservoirs. These tasks aim to improve short-term and medium-term operational planning by integrating advanced data-driven techniques into hydrological forecasting chains. Five initial validation sites, three located in Spain and two in Greece, were analyzed using a suite of statistical and machine learning techniques, including ARIMA, LSTM, and Random Forest (RF) models implemented in MATLAB. The first results demonstrated satisfactory forecasting performance for regulated basins, particularly for the fourth and fifth reservoirs of the five-reservoir cascade system along the Aliakmon River in Greece. When extending the workflow to natural inflows and incorporating the first reservoir of the cascade as a new validation site, the modelling framework revealed a significant improvement in simulated inflows, emphasizing the importance of upstream regulation effects in data-driven forecasting. To further strengthen the robustness and reliability of the methodology, a sixth validation site, corresponding to the upstream (first) reservoir, was subsequently integrated into the project s evaluation structure. This paper presents the overall methodological framework, data preprocessing strategies, selected modelling tools, and preliminary forecasting results for the three Greek validation sites: Asomata, Agia Varvara, and Ilarion on the Aliakmon River. The study highlights the potential of statistical and machine learning approaches to support hydropower optimization within interconnected and regulated river systems.

  PublisherDOI

• Multicriteria Approach to Selecting Sites for Pumped-Storage Hydropower Plants

  2025-11-26

  articleSenior author
  PublisherDOI

• Modelling inflows in Ilarion reservoir, Greece, using HEC-HMS

  E3S Web of Conferences · 2025-01-01

  articleOpen access

  This study investigates the application of the Hydrologic Modeling System (HEC-HMS) for simulating inflows to the Ilarion Reservoir, the first reservoir in the cascade of five along the Aliakmon River in northern Greece. The model is calibrated using observed meteorological and hydrological data to assess its performance and reliability in reproducing observed flows. Model accuracy is evaluated through statistical indicators such as the Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS). The results demonstrate a satisfactory agreement between simulated and observed discharge, highlighting the model’s potential as a reliable tool for hydrological forecasting and water resource management in the region.

  PublisherOA PDFDOI

• Combining Hydrodynamic Modelling and Solar Potential Assessment to Evaluate the Effects of FPV Systems on Mihăilești Reservoir, Romania

  Hydrology · 2025-06-19 · 1 citations

  articleOpen access

  Floating photovoltaic (FPV) systems are a new green technology emerging lately, having the indisputable advantage of not covering agricultural land but instead the surface of lakes or reservoirs. Being a new technology, even though the number of studies is significant, reliable results remain limited. This paper presents the possible influence of an FPV farm installed on the surface of a reservoir in Romania in four scenarios of the surface being covered with photovoltaic panels. The changes in the water mass under the FPV panels were determined using mathematical modelling as a tool. For this purpose, a water quality model was implemented for Mihăilești Reservoir, Romania, and the variations in the temperature, the phytoplankton biomass, and the total phosphorus and nitrogen were computed. Also, by installing FPV panels, it was estimated that a volume of water of between 1.75 and 7.43 million m3/year can be saved, and the greenhouse gas emission reduction associated with the proposed solutions will vary between 15,415 and 66,066 tCO2e/year; these results are in agreement with those reported in other scientifical studies. The overall conclusion is that the effect of an FPV farm on the reservoir’s surface is beneficial for the water quality in the reservoir.

  PublisherOA PDFDOI

• Hybrid LSTM-ARIMA Model for Improving Multi-Step Inflow Forecasting in a Reservoir

  Water · 2025-10-24 · 5 citations

  articleOpen accessSenior author

  In the hydropower sector, accurate estimation of short-term reservoir inflows is an essential element to ensure efficient and safe management of water resources. Short-term forecasting supports the optimization of energy production, prevention of uncontrolled water discharges, planning of equipment maintenance, and adaption of operational strategies. In the absence of data on topography, vegetation, and basin characteristics (required in distributed or semi-distributed models), data-driven approaches can serve as effective alternatives for inflow prediction. This study proposes a novel hybrid approach that reverses the conventional LSTM (Long Short-Term Memory)—ARIMA (Autoregressive Integrated Moving Average) sequence: LSTM is first used to capture nonlinear hydrological patterns, followed by ARIMA to model residual linear trends.The model was calibrated using daily inflow data in the Izvorul Muntelui–Bicaz reservoir in Romania from 2012 to 2020, tested for prediction on the day ahead in a repetitive loop of 365 days corresponding to 2021 and further evaluated through multiple seven-day forecasts randomly selected to cover all 12 months of 2021. For the tested period, the proposed model significantly outperforms the standalone LSTM, increasing the R2 from 0.93 to 0.96 and reducing RMSE from 9.74 m3/s to 6.94 m3/s for one-day-ahead forecasting. For multistep forecasting (84 values, randomly selected, 7 per month), the model improves R2 from 0.75 to 0.89 and lowers RMSE from 18.56 m3/s to 12.74 m3/s. Thus, the hybrid model offers notable improvements in multi-step forecasting by capturing both seasonal patterns and nonlinear variations in hydrological data. The approach offers a replicable data-driven solution for inflow prediction in reservoirs with limited physical data.

  PublisherDOI

• ELEPHANT HERDING OPTIMIZATION FOR ENHANCED FORECASTING OF INFLOW TIME SERIES USING STATISTICAL AND MACHINE LEARNING MODELS

  International Multidisciplinary Scientific GeoConference SGEM ... · 2025-12-27

  articleSenior author

  Accurate forecasting of reservoir inflows based on historical data is essential for effective water resources planning and management. Water flow forecasting presents a major challenge due to the nonlinear, non-stationary, stochastic, and highly noisy nature of water flows in rivers and inflows into reservoirs. These complex characteristics hinder the modeling process and limit the effectiveness of conventional prediction methods, making it difficult to achieve high accuracy results. This study explores the use of the Elephant Herding Optimization (EHO) algorithm for hyperparameter tuning in three established forecasting models: Autoregressive Integrated Moving Average (ARIMA), Gated Recurrent Unit (GRU), and Random Forest (RF). The proposed framework enables efficient exploration of the parameter space and adaptive learning of inflow patterns, aiming to reduce overfitting and improve predictive accuracy. All models are trained using historical inflow data and evaluated for one-day-ahead forecasts over a 365-day period in an open-loop configuration. By combining data-driven methods with meta-heuristic optimization, this work contributes to the development of robust forecasting tools for water resources management, enhancing the resilience and sustainability of hydropower system operation.

  PublisherDOI

Frequent coauthors

• Florica Popa

  Valahia University of Targoviste

  21 shared

• Gabriela Elena Dumitran

  Universitatea Națională de Știință și Tehnologie Politehnica București

  16 shared

• Ivaylo Stoyanov

  Angel Kanchev University of Ruse

  16 shared

• Teodor Iliev

  Angel Kanchev University of Ruse

  16 shared

• Assoc Gani Balbayev

  L. N. Gumilyov Eurasian National University

  16 shared

• Angela Neagoe

  Universitatea Națională de Știință și Tehnologie Politehnica București

  13 shared

• Liana Ioana Vuţă

  Universitatea Națională de Știință și Tehnologie Politehnica București

  11 shared

• Eliza-Isabela Tică

  10 shared

Labs

• Bogdan Popa's Research GroupPI

Education

• Engineer, Hydraulics

  Universitatea Politehnica din București

  1990

Awards & honors

• Fellow of the Acoustical Society of America (2023)
• NSF CAREER Award (2020)